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Deep Learning & Machine Learning

Machine Learning That Even My Mom Can Do (Basic Practice)

The first step in learning AI: We've created the perfect curriculum for AI beginners! Second, match the theories and code you've learned! Now it's time for the real world! This is the stage to solidify the foundation and confidence in your knowledge by unraveling the theories you've learned into code.

(5.0) 2 reviews

41 learners

Level Beginner

Course period Unlimited

  • yc
EDA
EDA
Machine Learning(ML)
Machine Learning(ML)
EDA
EDA
Machine Learning(ML)
Machine Learning(ML)

What you will gain after the course

  • ⭐ Model Parameters VS Hyperparameters

  • ⭐ Understanding How Hyperparameters Work for Each Model

  • ⭐ Basic Knowledge for Handling Data

  • ⭐ Practice with Classification, Regression, Clustering, and Recommendation Systems

  • ⭐ Hyperparameter Tuning Techniques

📢 This lecture is for non-majors.

Artificial intelligence, solved very easily!

I have excluded statistical and mathematical concepts as much as possible!

Theory lectures, don't be afraid!

Machine Learning That Even Our Mom Can Do (Basic Practice)

Artificial Intelligence (AI)

Machine Learning

Scikit-Learn

Course Introduction

  • Theory is the stepping stone for various code applications.

  • As a non-major, I won the grand prize and excellence award in contests, the first prize in competitions, and the grand prize and excellence award for projects in just 5 months .

  • You need to know the principles to be able to apply them to various situations and data.

  • When learning about artificial intelligence for the first time, various terms are thrown around, and the learning order felt unfamiliar while learning according to the curriculum at the institution . We thought a lot about it and modified and arranged the order so that even beginners can follow along with as little inconvenience as possible .

  • Rather than simply explaining mathematical and statistical concepts, when related references are made to models or indicators, the necessity of the corresponding formula or concept is mentioned, making it much easier to understand and convince, making learning smoother.

Lecture outline

  • We use only the minimum mathematical and statistical concepts necessary for understanding, and even then, we have organized them all into examples for easy understanding.

  • By applying what you learned in the theory lectures directly to practice, you can check your knowledge and apply the content to code, completely destroying your fears and worries about entering the field of artificial intelligence.

  • Those who do not know about machine learning will be able to learn systematically and broadly without feeling burdened, and those who know about machine learning will be able to establish the concept accurately once again.

  • This is a basic course that covers almost all parts of machine learning that can be intuitively understood, and concepts such as SVM, ROC-AUC, and dimensionality reduction natural language processing (NLP) are covered in the advanced machine learning theory.

  • Since all lectures focus on future deep learning lectures, we recommend this course to those who want to build a solid foundation from machine learning .

How the training is conducted

Get the practice problem file and look at it first.

Watch the lecture and fill in the blanks in the questions to understand them.

Practical Lecture STEP

1. Model parameters VS hyperparameters (theory)

Time to understand exactly what hyperparameters are for practice

2. Decision Tree Hyperparameters (Theory)

Time to understand exactly what Decision Tree hyperparameters are

3. Ensemble Hyperparameters (Theory)

Time to understand Voting, Bagging, Boosting hyperparameters precisely

4. Classification Practice (Practice)

Time to learn classification models and tune hyperparameters using the models you learned.

4. Linear Regression Model Hyperparameters (Theory)

Why there are no hyperparameters for LinearRegression, Ridge, Lasso, ElasticNet, PolynomialFeatures hyperparameters

Time to understand exactly about

5. Regression Practice (Practice)

Time to directly practice model learning and hyperparameter tuning using regression models and CART models learned.

6. Clustering hyperparameters (theory)

Time to understand exactly about K-means, Mean-shift, GMM, DBSCAN hyperparameters

7. Clustering Practice (Practice)

Time to learn model learning, hyperparameter tuning, and result analysis using the clustering models we learned.

8. Recommendation System Practice (Practice)

Time to check the operation of the recommendation system algorithm learned in the theory lecture through code.

9. Hyperparameter optimization technique + final practice (theory + practice)

Time to briefly review optimization techniques and conduct a hands-on exercise to summarize everything.

Step-by-step learning content

This course is the second of five curricula. The remaining curricula will be released sequentially.

Lecture Features

🎯 This course consists of theory lectures and code practice for code practice.

🎯 PPT and code practice materials provided

Lecture Preview

Data preprocessing Skew data log application principle

Decision Tree Visualization and Learning Strategy

Decision Tree Hyperparameters Description

Light GBM Hyperparameters Description

LinearRegression(Gradient Descent vs OLS)

AdaBoost hyperparameters explained

Recommended for
these people

Who is this course right for?

  • ⭐ Machine Learning Even My Mom Can Do (Basic Theory) Student

  • ⭐ Those who need to understand hyperparameters

  • ⭐ People who want to do machine learning-related practice

Need to know before starting?

  • Python Basics

  • NumPy, Pandas Basics

Hello
This is

208

Learners

18

Reviews

5

Answers

4.9

Rating

3

Courses

Because I am a non-major, I understand non-majors well.

I will do my best to help you from a non-major's perspective.

Completed the 5th cohort of the AI Academy Grand Prize in Time-Series Agricultural Product Price Prediction Project 1st Place in Kaggle Competition (out of 200) Excellence Award in Object Detection and RAG-based Mock Interview Project Korean

Completed the 5th cohort of the Artificial Intelligence Academy

Grand Prize in Time-Series Agricultural Product Price Prediction Project

1st Place in Kaggle Competition (out of 200 participants)

Excellence Award for Object Detection and RAG-based Mock Interview Project

Grand Prize at the AI-based Social Problem Solving Contest hosted by the Korea Artificial Intelligence Association

Excellence Award, Honam ICT Innovation Digital New Technology Contest

Curriculum

All

16 lectures ∙ (6hr 47min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

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2 reviews

5.0

2 reviews

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